Using probabilistic estimation of expression residuals (PEER) to obtain increased power and interpretability of gene expression analyses

Abstract

We present PEER (probabilistic estimation of expression residuals), a software package implementing statistical models that improve the sensitivity and interpretability of genetic associations in population-scale expression data. This approach builds on factor analysis methods that infer broad variance components in the measurements. PEER takes as input transcript profiles and covariates from a set of individuals, and then outputs hidden factors that explain much of the expression variability. Optionally, these factors can be interpreted as pathway or transcription factor activations by providing prior information about which genes are involved in the pathway or targeted by the factor. The inferred factors are used in genetic association analyses. First, they are treated as additional covariates, and are included in the model to increase detection power for mapping expression traits. Second, they are analyzed as phenotypes themselves to understand the causes of global expression variability. PEER extends previous related surrogate variable models and can be implemented within hours on a desktop computer.

Genetic effects on gene expression across human tissues

“…Expression measurements for each gene in each tissue were subsequently transformed to the quantiles of the standard normal distribution. The effects of unobserved confounding variables on gene expression were quantified with PEER 12 , run independently for each tissue. Fifteen PEER factors were identified for tissues with fewer than 150 samples; 30 for tissues with sample sizes between 150 and 250; and 35 for tissues with more than 250 tissues.…”

“…However, the majority of cis -eQTLs do exhibit allele specific expression. We applied a linear model controlling for ancestry, sex, genotyping platform and latent factors 12 in the expression data for each tissue that may reflect batch or other technical variables (see Methods; Extended Data Fig. 1, Supplementary Information 6 and Supplementary Figs 7–10).…”

“…Disambiguating true broad regulatory effects from artefacts remains an important challenge 29 . In our primary analysis, we applied aggressive correction of potential confounders, controlling for 15–35 probabilistic estimation of expression residuals (PEER) factors 12 capturing 59–78% of total variance in gene expression levels (Supplementary Information 5). However, PEER and related approaches 30 may also remove variance in gene expression levels arising from regulatory pathways and broad trans effects.…”

Dynamic Role of trans Regulation of Gene Expression in Relation to Complex Traits

“…Twenty PEER factors were calculated using a Bayesian framework and were used to account for hidden confounding factors in the adjusted gene expression data. 23 For each SNP-mRNA pair, a linear model was developed to identify SNP-mRNA associations, adjusting for PEER factors and familial relationship. p values were adjusted for multiple comparisons using the false discovery rate (FDR) Figure 1.…”

Genetic Regulation of Adipose Gene Expression and Cardio-Metabolic Traits

“…We applied the probabilistic estimation of expression residuals (PEER) method 27 to infer and account for complex non-genetic factors affecting gene expression levels. This method is designed to detect the maximum number of cis-eQTLs.…”